Impact of a meaning‐centered intervention on job satisfaction and on quality of life among palliative care nurses
Bibliographic record
Abstract
OBJECTIVE: Palliative care (PC) nurses experience several recurrent organizational, professional, and individual challenges. To address existential and emotional demands, the meaning-centered intervention was recently developed. The intervention applied didactic and process-oriented strategies, including guided reflections, experiential exercises, and education based on themes of Viktor Frankl's logotherapy. The objective of this study was to test its efficiency to improve job satisfaction and quality of life in PC nurses from three regional districts in Quebec Province, Canada. METHODS: A randomized waiting-list group design was conducted, intervention group (n=56) versus waiting-list group (n=53). Job satisfaction, perception of benefits of working in PC, and spiritual and emotional quality of life were measured at pre-, posttest, and 3-month follow-up. RESULTS: The PC nurses in the experimental group reported more perceived benefits of working in PC after the intervention and at follow-up. Spiritual and emotional quality of life remained, however, unaffected by the intervention. CONCLUSIONS: To explain null findings, theoretical and methodological challenges, related to existential interventions, such as choice of outcomes, and selection bias (participants recruited were healthy workers) are discussed. Future directions and strategies to deal with those issues are proposed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".